Yuhan Xie

EA-PheWAS improves gene-phenotype discovery by integrating phenotype embeddings with PheWAS, outperforming conventional methods in detecting associations.

This study identifies genetic loci influencing immune cell proportions and their associations with autoimmune diseases using GWAS and cWAS.

PERADIGM identifies rare disease-associated genes by integrating phenotype embeddings and patient similarity, outperforming traditional methods in detecting candidate genes for ADPKD, Marfan syndrome, and NF1.

This study uses AlphaFold 2 to analyze the structural impact of alternative splicing in 3,000 human genes, identifying structural changes and disease-related mutations.

The paper introduces MAJAR, a statistical framework to assess biomarker replicability in meta-analysis, showing improved power and robustness compared to existing methods.

The paper uses whole-exome sequencing to identify rare genetic variants in CYP2R1 associated with ischemic stroke risk and related brain imaging traits.

The paper uses a Markov Random Field model with protein-protein interaction data to identify 46 candidate genes for congenital heart disease, including known and highly expressed genes.

M-DATA improves statistical power for identifying risk genes by jointly analyzing de novo mutations across multiple traits, demonstrating its effectiveness in congenital heart disease and autism.